Accurate information on the ice thickness of Svalbard's glaciers is important for assessing the contribution to sea level rise in a present and a future climate. However, direct observations of the glacier bed are scarce. Here, we use an inverse approach and high-resolution surface observations to infer basal conditions. We present and analyse the new bed and thickness maps, quantify the ice volume (6800 km³), and compare these against radar data and previous studies.

Some fjords contain dense packs of icebergs referred to as ice mélange. Ice mélange can affect the stability of marine-terminating glaciers by resisting the calving of new icebergs and by modifying fjord currents and water properties. We have developed the first numerical model of ice mélange that captures its granular nature and that is suitable for long-timescale simulations. The model is capable of explaining why some glaciers are more strongly influenced by ice mélange than others.

This study used a machine learning approach to estimate the densities over the Antarctic Ice Sheet, particularly in the areas where the snow is usually dry. The motivation is to establish a link between satellite parameters to snow densities, as measurements are difficult for people to take on site. It provides valuable insights into the complexities of the relationship between satellite parameters and firn density and provides potential for further studies.

Anthropogenic warming is causing accelerated Greenland ice sheet melt. Here, we use a computer model to understand how prolonged warming and ice melt could threaten ice sheet stability. We find a threshold beyond which Greenland will lose more than 80 % of its ice over several thousand years, due to the interaction of surface and solid-Earth processes. Nearly complete Greenland ice sheet melt occurs when the ice margin disconnects from a region of high elevation in western Greenland.